首页|Advances of machine learning in materials science:Ideas and techniques
Advances of machine learning in materials science:Ideas and techniques
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In this big data era,the use of large dataset in conjunction with machine learning(ML)has been increasingly popular in both industry and academia.In recent times,the field of materials science is also undergoing a big data revolution,with large database and repositories appearing everywhere.Traditionally,materials science is a trial-and-error field,in both the computational and experimental departments.With the advent of machine learning-based techniques,there has been a paradigm shift:materials can now be screened quickly using ML models and even gener-ated based on materials with similar properties;ML has also quietly infil-trated many sub-disciplinary under materials science.However,ML remains relatively new to the field and is expanding its wing quickly.There are a plethora of readily-available big data architectures and abun-dance of ML models and software;The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research.In this review,we attempt to provide an intro-duction and reference of ML to materials scientists,covering as much as possible the commonly used methods and applications,and discussing the future possibilities.
machine learningmaterials science
Sue Sin Chong、Yi Sheng Ng、Hui-Qiong Wang、Jin-Cheng Zheng
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Department of New Energy Science and Engineering,Xiamen University Malaysia,Sepang 43900,Malaysia
Engineering Research Center of Micro-nano Optoelectronic Materials and Devices,Ministry of Education
Fujian Key Laboratory of Semiconductor Materials and Applications,Cl Center for OSED,and Department of Physics,Xiamen University,Xiamen 361005,China
Ministry of Higher Education Malaysia through the Fundamental Research Grant Scheme
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